Comparison of Augmented and Non-augmented GPS Receivers for Transportation Applications: Field Survey and Analysis
Bibliographic record
Abstract
Currently, the use of global position system (GPS) for tracking and navigation purposes is considered a state-of-the-practice in vehicle location applications. For most applications, the general accuracies provided by a non-augmented GPS receiver is sufficient. However, for emerging transportation applications that require the determination of the lane of travel for a particular vehicle, higher tracking accuracies may be required. Such accuracy levels can be achieved with the use of GPS receivers augmented with differential correction. The purpose of this research is to conduct a preliminary analysis to compare GPS receivers without DGPS corrections to those supported by augmentation systems. Three GPS receiver setups were compared, one supported by the Wide Area Augmentation System (WAAS), one supported by the Canada-wide Differential GPS (CDGPS) service, and a third that did not incorporate any augmentation (i.e. a basic receiver). Based on field data collected in Vancouver, B.C., the results showed that, in terms of reliability, the performance of the three receiver setups were consistent and comparable, however with the CDGPS-augmented setup slightly outperforming the WAAS-augmented and non-augmented setups. However, in terms of positional accuracies, identified by road lane differentiation, it was found that the differential-augmented receivers outperformed the non-augmented receiver. The results indicate that GPS receivers without augmentation systems are only suitable for applications that do not require highly accurate position information. There seem to be some potential in using differentially-corrected GPS receivers as complementary sensors for high-accuracy ITS applications such as VII/VIC, where need for high precision location information (i.e. lane of travel) is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".